T helper 17 cells may drive neuroprogression in major depressive disorder: proposal of an integrative model

The exact pathophysiology of major depressive disorder (MDD) remains elusive. The monoamine theory, which hypothesizes that MDD emerges as a result of dysfunctional serotonergic, dopaminergic and noradrenergic pathways, has guided the therapy of this illness for several decades. More recently, the involvement of activated immune, oxidative and nitrosative stress pathways and of decreased levels of neurotrophic factors has provided emerging insights regarding the pathophysiology of MDD, leading to integrated theories emphasizing the complex interplay of these mechanisms that could lead to neuroprogression. In this review, we propose an integrative model suggesting that T helper 17 (Th17) cells play a pivotal role in the pathophysiology of MDD through (i) microglial activation, (ii) interactions with oxidative and nitrosative stress, (iii) increases of autoantibody production and the propensity for autoimmunity, (iv) disruption of the blood-brain barrier, and (v) dysregulation of the gut mucosa and microbiota. The clinical and research implications of this model are discussed.

Effect of the toxic milk mutation (tx) on the function and intracellular localization of Wnd, the murine homologue of the Wilson copperATPase

Wilson disease is an autosomal recessive copper transport disorder resulting from defective biliary excretion of copper and subsequent hepatic copper accumulation and liver failure if not treated. The disease is caused by mutations in the ATP7B (WND) gene, which is expressed predominantly in the liver and encodes a copper-transporting P-type ATPase that is structurally and functionally similar to the Menkes protein (MNK), which is defective in the X-linked copper transport disorder Menkes disease. The toxic milk (tx) mouse has a clinical phenotype similar to Wilson disease patients and, recently, the tx mutation within the murine WND homologue (Wnd) of this mouse was identified, establishing it as an animal model for Wilson disease. In this study, cDNA constructs encoding the wild-type (Wnd-wt) and mutant (Wnd-tx) Wilson proteins (Wnd) were generated and expressed in Chinese hamster ovary (CHO) cells. The tx mutation disrupted the copper-induced relocalization of Wnd in CHO cells and abrogated Wnd-mediated copper resistance of transfected CHO cells. In addition, co-localization experiments demonstrated that while Wnd and MNK are located in the trans-Golgi network in basal copper conditions, with elevated copper, these proteins are sorted to different destinations within the same cell. Ultrastructural studies showed that with elevated copper levels, Wnd accumulated in large multi-vesicular structures resembling late endosomes that may represent a novel compartment for copper transport. The data presented provide further support for a relationship between copper transport activity and the copper-induced relocalization response of mammalian copper ATPases, and an explanation at a molecular level for the observed phenotype of tx mice

Node localization using mobile robots in delay-tolerant sensor networks

We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.

Sensor fusion based localization of a mobile user in a wireless network

This paper applies sensor fusion to the localization problem of a mobile user. We propose that the use of direction of arrival (DOA) estimations along with received signal strength measurements can increase the accuracy and robustness of location estimations. The DOA estimations are incapable of providing multi-dimensional positioning alone, while signal strength methods are prone to high uncertainties. A Robust Extended Kalman Filter (REKF) is used to derive the state estimate of the mobile user's position, and successfully track the mobile users with less system complexity, as it requires measurements from only one base station. Therefore, localization of mobile users can be performed at the single base station. Furthermore, the technique is robust against system uncertainties caused by the inherent deterministic nature of the mobility model. Through simulation, we show the accuracy of our prediction algorithm and the simplicity of its implementation.

Passive angle measurement based localization consistency via geometric constraints

In this paper, we examine the geometric relations between various measured parameters and their corresponding errors in angle-measurement based emitter localization scenarios. We derive a geometric constraint formulating the relationship among the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry.

Optimality analysis of sensor-target geometries in Passive localization: Part 1 - bearing-only localization

In this paper we characterize the relative sensor-target geometry for bearing-only localization in R2. We analyze the geometry in terms of the Cramer-Rao inequality and the corresponding Fisher information matrix, aiming to characterize and state explicit results in terms of the potential localization performance. In particular, a number of interesting results are rigorously derived which highlight erroneous assumptions often made in the existing literature.

Optimality analysis of sensor-target geometries in passive localization: Part 2 - time-of-arrival based localization

In this paper we characterize the relative sensor-target geometry in R2 in terms of potential localization performance for time-of-arrival based localization. Our aim is to characterize those relative sensor-target geometries which minimize the relative Cramer-Rao lower bound.

Exploiting geometry for improved hybrid AOA/TDOA-based localization

In this paper we examine the geometrically constrained optimization approach to localization with hybrid bearing (angle of arrival, AOA) and time difference of  arrival (TDOA) sensors. In particular, we formulate a constraint on the measurement errors which is then used along with constraint-based optimization tools in order to estimate the maximum likelihood values of the errors given an appropriate cost function. In particular we focus on deriving a localization algorithm for stationary target localization in the so-called adverse localization geometries where the relative positioning of the sensors and the target do not readily permit accurate or convergent localization using traditional approaches. We illustrate this point via simulation and we compare our approach to a number of different techniques that are discussed in the literature.

Optimal range-difference-based localization considering geometrical constraints

This paper proposes a new type of algorithm aimed at finding the traditional maximum-likelihood (TML) estimate of the position of a target given time-difference-of-arrival (TDOA) information, contaminated by noise. The novelty lies in the fact that a performance index, akin to but not identical with that in maximum likelihood (ML), is a minimized subject to a number of constraints, which flow from geometric constraints inherent in the underlying problem. The minimization is in a higher dimensional space than for TML, and has the advantage that the algorithm can be very straightforwardly and systematically initialized. Simulation evidence shows that failure to converge to a solution of the localization problem near the true value is less likely to occur with this new algorithm than with TML. This makes it attractive to use in adverse geometric situations.

Bearing-only localization using geometrically constrained optimization

We examine the problem of optimal bearing-only localization of a single target using synchronous measurements from multiple sensors. We approach the problem by forming geometric relationships between the measured parameters and their corresponding errors in the relevant emitter localization scenarios. Specifically, we derive a geometric constraint equation on the measurement errors in such a scenario. Using this constraint, we formulate the localization task as a constrained optimization problem that can be performed on the measurements in order to provide the optimal values such that the solution is consistent with the underlying geometry. We illustrate and confirm the advantages of our approach through simulation, offering detailed comparison with traditional maximum likelihood (TML) estimation.

N-acetyl cysteine for depressive symptoms in bipolar disorder - a double blind, randomized, placebo-controlled trial

Background: Treatment-resistant subthreshold depression is a major problem in bipolar disorder. Both depression and bipolar disorderare complicated by glutathione depletion. We hypothesized that treatment with N-acetyl cysteine (NAC), a safe, orally bioavailable precursor of glutathione, may improve the depressive component of bipolar disorder.

Methods: A randomized, double-blind, multicenter, placebo-controlled study of individuals (n 75) with bipolar disorder in the maintenance phase treated with NAC (1 g twice daily) adjunctive to usual medication over 24 weeks, with a 4-week washout. The two primary outcomes were the Montgomery Asberg Depression Rating Scale (MADRS) and time to a mood episode. Secondary outcomes included the Bipolar Depression Rating Scale and 11 other ratings of clinical status, quality of life, and functioning.

Results: NAC treatment caused a significant improvement on the MADRS (least squares mean difference [95% confidence interval]: 8.05 [13.16, 2.95], p .002) a n d most secondary scales at end point. Benefit was evident by 8 weeks on the Global Assessment of Functioning Scale and Social and Occupational Functioning Assessment Scale and at 20 weeks on the MADRS. Improvements were lost after washout. There was no effect of NAC on time to a mood episode (log-rank test: p .968) and no significant between-group differences inadverse events. Effect sizes at end point were medium to high for improvements in MADRS and 9 of the 12 secondary readouts.

Conclusions: NAC appears a safe and effective augmentation strategy for depressive symptoms in bipolar  disorder.

Effects of N-acetylcysteine on substance use in bipolar disorder : a randomized placebo controlled clinical trial

Objective: To evaluate the effect of N-acetylcysteine (NAC) on substance use in a double-blind, placebo-controlled trial of NAC in bipolar disorder. It is hypothesised that NAC will be superior to placebo for reducing scores on the Clinical Global Impressions scale for Substance Use (CGI-SU).

Methods: Participants were randomised to 6-months of treatment with 2 g/day NAC (n = 38) or placebo (n = 37). Substance use was assessed at baseline using the Habits instrument. Change in substance use was assessed at regular study visits using the CGI-SU.

Results: Amongst the 75 participants 78.7% drank alcohol (any frequency), 45.3% smoked tobacco and 92% consumer caffeine. Other substances were used by fewer than six participants. Caffeine use was significantly lower for NAC-treated participants compared with placebo at week 2 of treatment but not at other study visits.

Conclusion: NAC appeared to have little effect on substance use in this population. A larger study on a substance using population will be necessary to determine if NAC may be a useful treatment for substance use.